






"""simple docstring"""
import numpy

# List of input, output pairs
lowerCamelCase_							      = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCamelCase_							      = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase_							      = [2, 4, 1, 5]
lowerCamelCase_							      = len(train_data)
lowerCamelCase_							      = 0.009

def 							snake_case (  A__      ,A__="train"		):
			return calculate_hypothesis_value(A__      ,A__		) - output(
			    A__      ,A__		)

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Tuple     	=   0
			for i in range(len(A__		) - 1		):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val

def 							snake_case (  A__      ,A__		):
			if data_set == "train":
						return train_data[example_no][1]
			elif data_set == "test":
						return test_data[example_no][1]
			return None

def 							snake_case (  A__      ,A__		):
			if data_set == "train":
						return _hypothesis_value(train_data[example_no][0]		)
			elif data_set == "test":
						return _hypothesis_value(test_data[example_no][0]		)
			return None

def 							snake_case (  A__      ,A__=m		):
			UpperCAmelCase_	:					Optional[int]     	=   0
			for i in range(A__		):
						if index == -1:
									summation_value += _error(A__		)
						else:
									summation_value += _error(A__		) * train_data[i][0][index]
			return summation_value

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Dict     	=   summation_of_cost_derivative(A__      ,A__		) / m
			return cost_derivative_value

def 							snake_case (  ):
			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			UpperCAmelCase_	:					Dict     	=   0.000002
			UpperCAmelCase_	:					Optional[int]     	=   0
			UpperCAmelCase_	:					Tuple     	=   0
			while True:
						j += 1
						UpperCAmelCase_	:					Tuple     	=   [0, 0, 0, 0]
						for i in range(0      ,len(A__		)		):
									UpperCAmelCase_	:					Dict     	=   get_cost_derivative(i - 1		)
									UpperCAmelCase_	:					Optional[Any]     	=   (
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    A__      ,A__      ,atol=A__      ,rtol=A__      ,):
									break
						UpperCAmelCase_	:					str     	=   temp_parameter_vector
			print(("Number of iterations:", j)		)

def 							snake_case (  ):
			for i in range(len(A__		)		):
						print(("Actual output value:", output(A__      ,"test"		))		)
						print(("Hypothesis output:", calculate_hypothesis_value(A__      ,"test"		))		)


if __name__ == "__main__":
			run_gradient_descent()
			print('''\nTesting gradient descent for a linear hypothesis function.\n''')
			test_gradient_descent()






__snake_case				:      int   		=			[
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


